The Tech Trek Why Enterprise AI Fails Without Better Data and Business Process Design
Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time.
This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments.
In this episode
Why AI has made data governance and data lineage far more operational
Why business process clarity matters before data architecture or tooling decisions
How real time AI changes the demands on data quality and system design
Where agentic AI fits, from workflow automation to more advanced decision support
Why human judgment still matters in AI systems shaped by risk, ethics, and security
Timestamped highlights
01:47 Why AI raises the stakes for governance, lineage, and trust in data
04:57 Why business architecture has to lead before technical design
09:11 The progression from predictive models to agentic AI workflows
17:55 Why the human in the loop is still essential
21:16 What makes an AI project worth prioritizing
26:06 What has changed, and what has not, in AI related change management
Standout line
“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.”
Practical takeaway
If you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail.
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